High-throughput sequencing reveals that microRNA from exosomes as potential pathogenetic factors and biomarkers of moyamoya disease

doi:10.21203/rs.3.rs-1218259/v2

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High-throughput sequencing reveals that microRNA from exosomes as potential pathogenetic factors and biomarkers of moyamoya disease

https://doi.org/10.21203/rs.3.rs-1218259/v2

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Background: As a progressive cerebrovascular disease, moyamoya disease is a common cause of stroke in children and adults; however, early biomarkers and the pathogenesis of moyamoya disease remain poorly understood.

Objective: We aimed todiagnose moyamoya disease by identifying noninvasive rapid liquid diagnostic biomarkers using plasma exosomal microRNAs (miRNAs).

Methods and Material: For the first time, frozen plasma samples from moyamoya disease patients, next-generation high-throughput sequencing technologies, real-time quantitative PCR, gene ontology analysis and Kyoto Encyclopaedia of Genes and Genomes pathway analysis analyses of exosomal miRNA-related target gene functions were used to identify plasma exosomal miRNAs as potential biomarkers of moyamoya disease. The area under the curve of the receiver operating characteristic (ROC) curve was used to evaluate the sensitivity and specificity of biomarkers for predicting events.

Results: The target genes of the different expression miRNA showed that they were mainly enriched in axon guidance, regulation of the actin cytoskeleton and the MAPK signalling pathway. Ten miRNAs (miR-1306-5p, miR-196b-5p, miR-19a-3p, miR-22-3p, miR-320b, miR-34a-5p, miR-485-3p, miR-489-3p, miR-501-3p, and miR-487-3p) were found to be associated with the most sensitive and speciﬁc pathways for predicting moyamoya disease.

Conclusions: Plasma exosomal miRNAs could be used to construct an accurate predictive model for improving moyamoya disease detection.

moyamoya disease

exosomes microRNA

the actin cytoskeleton signalling pathway

plasma

biomarkers

Plasma exosomal miRNAs could be used to construct an accurate predictive model for improving moyamoya disease detection.

Moyamoya disease is a cerebrovascular disease with unknown aetiology and progressive, occlusive and abnormal collateral vascular networks. However, its pathogenesis is still unknown ¹. The disease usually involves both hemispheres, although in some patients, arterial stenosis or occlusion occurs only on one side, which is called unilateral moyamoya disease ². A more specific definition of moyamoya disease is idiopathic occlusion of bilateral vessels in the "Willis" arterial ring. The incidence rate of moyamoya disease in Northeast Asia is higher than that in other regions, especially in Japan, Korea, and China ³. The incidence and prevalence of MMD in mainland China as 0.59 and 1.01 per 100 000 person-years⁴, respectively. MMD maintains a classical pattern of bimodal age distribution. The first peak occurs around the age of five and the second one around the age of 40. The incidence rate of women is higher than that of men (female-to-male ratios is 1.12)⁴.

The pathogenesis of moyamoya disease is not clear and may be related to genetics, immunity, inflammation and other factors. Ring finger protein 213 (RNF213) has recently been identified as a susceptibility gene for moyamoya disease ^{5, 6}. Studies have confirmed that the apoptosis-inducing function of the RNF213 gene may be negatively regulated by its ubiquitin ligase, which is highly related to the occurrence of moyamoya disease ⁷. However, Sonobe et al. found that mice lacking the RNF213 gene did not spontaneously produce moyamoya disease, which indicated that the loss of function of RNF213 could not fully induce moyamoya disease ⁸. Therefore, although RNF213 may be a pathogenic factor of moyamoya disease, its specific mechanism in the pathogenesis of moyamoya disease is still unclear. Moyamoya disease is associated with the class I and class II genes of human leukocyte antigen (HLA), although the related genetic genes differ significantly among different races ⁹. In moyamoya disease, when smooth muscle proliferates, macrophages and T lymphocytes infiltrate the intimal surface, resulting in intimal hyperplasia and lumen stenosis, which may be the main inflammatory mechanism of the disease ¹⁰. As multimodal three-dimensional angiography technology matures, our understanding of moyamoya disease and the standardization of diagnostic standards have improved, which has increased the worldwide detection rate of moyamoya disease year by year. The treatment of moyamoya disease mainly revolves around surgical intervention. Direct and indirect revascularization surgeries are performed to improve blood circulation in the affected region¹¹. However, a clear conclusion has not been reached on the pathogenesis and evolution of moyamoya disease, and there is still a lack of effective biomarkers and molecular targeted therapies.

MicroRNAs (miRNAs) are single-stranded RNA molecules that measure approximately 21–23 nucleotides in length and participate in a lot of biological processes. Studies have found that exosomal miRNAs derived from cerebrospinal fluid of moyamoya disease patients may serve as a novel diagnostic biomarker for diagnosis ¹². Therefore, this study analysed the miRNAs expressed in the exosomes from plasma samples from moyamoya disease patients and healthy individuals via next-generation sequencing (NGS). This analysis quantified the expression profile of exosomal miRNAs and identified statistically correlated miRNAs to determine pathways that potentially mediate the pathogenesis and biomarkers of moyamoya disease.

2.1 Patients

Nine patients diagnosed with moyamoya disease by digital subtraction angiography were enrolled at Peking University Shenzhen Hospital from December 2020 to March 2021. Ten healthy individuals were recruited from the physical examination centre as the control group. All patients signed an informed agreement. For peripheral blood collection, 3 mL of peripheral blood from enrolled participants was collected into 5-mL K2EDTA Vacutainer tubes (Junnuo, Chengwu, China). The study was approved by the medical ethics committee of Peking University Shenzhen Hospital.

2.2 Isolation and identification of exosomes

The plasma supernatant was collected and centrifuged at 3500 g at 4 °C for 10 min, and then the supernatant was centrifuged at 12000 g at 4 °C for 10 min. The precipitate was discarded, and the supernatant was collected for exosome extraction. Exosomes were extracted based on exoQuick precipitation using an ExoQuick precipitation system (System Biosciences Inc., CA, United States). Nanoparticle tracking analysis (NTA), transmission electron microscopy (TEM), and Western Blot were used to identify the exosome features.

2.3 RNA library construction and sequencing

First, TRIzol reagent (Invitrogen Life Technologies, Carlsbad, CA) was used to extract total RNA from the exosome pellets following the manufacturer’s instructions. The total RNA concentration and quality were quantified using a Qubit3 Fluorometer (Thermo Fisher Scientific, MA, United States) and an Agilent Bioanalyser 2100 (Agilent Technologies, CA, United States).

The small RNA library was constructed according to the QIAseq miRNA Library kit (QIAGEN, Germany). Approximately 100 ng of total RNA was used to prepare the miRNA library, and it was supplemented with water to 20 μL. The library was constructed to ligate at the 3′ and 5′ ends of small RNA. Then, reverse transcription of the sample was started to obtain cDNA. QIAseq miRNA NGS beads were used to wash cDNA several times according to the manufacturer’s instructions. The concentration and quality of the small RNA library were analysed using an Agilent Bioanalyser 2100 (Agilent Technologies, CA, United States). The qualifying small RNA libraries were sequenced on a HiSeq 2500 (Illumina, San Diego, USA).

2.4 Data Processing and Bioinformatics Analysis

The small RNA sequencing raw data were processed to estimate microRNA expression. MiRNAs with an adjusted P value < 0.01 and fold change > 2 found by edgeR were considered differentially expressed. The miRNA targets were predicted using the prediction website TargetScan (http://www.targetscan.org/vert_71/). A Gene ontology (GO) enrichment analysis was performed via topGO, and a Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway analysis was performed using clusterProfiler (kobas2.0-20150126).

2.5 Real‑time quantitative PCR (RT-QPCR)

The miRNA expression levels were assessed by qRT–PCR. Total RNA and total RNA were reverse transcribed to cDNA using the PrimeScript First Strand cDNA Synthesis Kit (Takara, Beijing, China). RT-QPCR was carried out using 2X SYBR green qPCR mix (Takara, Beijing, China) and an ABI 7900HT sequence system (Thermo Fisher Scientific, Inc.). The reactions were incubated at 94 °C for 3 min, followed by 40 cycles at 95 °C for 15 s and 62 °C for 40 s. The primer sequences for the four miRNAs are shown in Supplement Table 1. U6 was used as an internal control of miRNAs. Statistical analyses of the results were performed using the 2−∆∆CT relative quantification method.

2.6 Statistical analysis

Student’s t test was applied to the RT-QPCR analysis results to compare the different groups using GraphPad Prism 5.0. ROC curves and AUC values were determined using edgeR package. GO and KEGG pathway analyses were assessed by Fisher’s exact test to identify significant results using the edgeR package. P values < 0.05 were significant.

3.1 Characterization of exosomes obtained from moyamoya disease patient plasma

The clinical information of the sample is shown in Table 1. According to whether the patient will see smoke-like changes in the brain during the digital subtraction cerebrovascular angiography examination, we can distinguish patients with moyamoya disease from patients with non-moyamoya disease. For example, Figures 1A and B show a patient with the same hemorrhagic moyamoya disease (bilateral), characterized by cerebral hemorrhage, occlusion of the end of the internal carotid artery on the right side, and smoke vessel formation). Figures 1C and D are the same healthy person, showing normal cerebral blood vessels. To characterize exosomes derived from moyamoya disease patient plasma, transmission electron microscopy, nanoparticle tracking analysis and western blotting were used to characterize exosome diameters and protein markers. CD63 and CD81 are protein markers of exosomes, and CD63 and CD81 are protein markers of exosomes, and were detected in NC exosomes, MMD exosomes and plasma by Western blotting analysis, it confirmed that we isolated exosomes (Figure 1E). Transmission electron microscopy showed a typical rounded morphology with a sagged double membrane (Figure 1F). The nanoparticle tracking analysis further confirmed that the exosome isolated from moyamoya disease patients and health people were 30–150 nm in diameter (Figure 1G). These results indicated that exosomes were successfully purified from all plasma samples.

3.2 GO and KEGG analyses of the target genes of differentially expressed miRNAs

Next, the raw data were obtained by high-throughput sequencing. The basic quality information on the reads showed that they can be used for subsequent data analyses (Table 2). A total of 1002 differentially expressed miRNAs were identified, and they included 585 upregulated and 417 downregulated miRNAs (Figure 2A). According to the GO analysis, the results indicated that these decreased expressed miRNAs were primarily associated with biological process terms, including regulation of cellular metabolic process, regulation of cellular process, cellular macromolecule metabolic process (Figure 2B). They were also were enriched in cellular component terms, including nucleoplasm, intracellular part (Figure 2C), and molecular function terms, including transcription regulatory region sequence-specific DNA binding and regulatory region nucleic acid binding (Figure 2D). These increased expressed miRNAs were primarily associated with biological process terms, including nervous system development, positive regulation of cellular process, positive regulation of biological process(Figure 2E); cellular component terms, including cytoplasm, intracellular, and intracellular part(Figure 2F), and molecular function terms, including protein binding and regulatory region nucleic acid binding (Figure 2G).The KEGG pathway analysis for these target genes showed that they were mainly enriched in axon guidance, regulation of the actin cytoskeleton and the MAPK signalling pathway (Figure 2H). There were 63 exosomal miRNAs involved in the axon guidance, regulation of actin cytoskeleton and MAPK signaling pathway (Figure 2I). Among them, the different target genes of 59 exosomal miRNAs were associated with the three pathways at the same time, especially with regulation of actin cytoskeleton (Figure 2J).

3.3 Prognosis of potential biomarkers by the receiver operating characteristic curve and area under curve

AUC of ROC were carried out determining the diagnostic values of these 63 miRNAs involved in the axon guidance, regulation of actin cytoskeleton and MAPK signaling pathway. Ten miRNAs (miR-1306-5p, miR-196b-5p, miR-19a-3p, miR-22-3p, miR-320b, miR-34a-5p, miR-485-3p, miR-489-3p, miR-501-3p, and miR-487b-3p) had signiﬁcantly diﬀerentiated moyamoya disease patients from healthy controls based on an AUC value above 0.9 and sensitivity and speciﬁcity above 0.8 (Figure 3). To further explore the expression of these exosomal miRNAs as potential biomarkers of moyamoya disease, RT-QPCR was performed to detect randomly the levels of these exosomal miRNAs extracted from the plasma samples of 9 moyamoya disease patients and 10 healthy individuals. These ten microRNAs showed the same expression patterns obtained from the high-throughput sequencing analysis. Hsa-miR-34a-5p, Hsa-miR-19a-3p, miR-22-3p, miR-196a-5p, miR-320b, miR-485-3p, miR-487b-3p, and miR-501-3p were up-regulated, and miR-489-3p and hsa-miR-1306-5p were downregulated (Figure 4).

In this study, we established a method for screening moyamoya disease-specific miRNAs from plasma exosomes. Although many studies have shown that secreted miRNA may represent potential biomarkers for many diseases, few studies have analysed the role of plasma-secreted miRNA in moyamoya disease ¹². To the best of our knowledge, this is the first study which successfully obtain exosomal miRNAs from a large number of plasma miRNAs and conduct a high-throughput sequencing analysis of the miRNAs, which can be used as biomarkers of clinical moyamoya disease and may be involved in the pathogenesis of moyamoya disease. Moyamoya disease is a disease of the central nervous system, and the molecular changes in cerebrospinal fluid may reflect the molecular conditions of the central nervous system¹³. But cerebrospinal fluid cannot be extracted for patients in certain age groups, and certain restrictions may also apply ^{14, 15}. Therefore, biomarkers in peripheral blood represent a valuable method of performing humoral diagnoses. Through the miRNA analysis of peripheral blood exosomes, we can develop a noninvasive rapid detection kit for the early diagnosis of moyamoya disease.

In this study, the top three signalling pathways enriched by the target genes of the differentially expressed exosomal miRNAs were axon guidance, regulation of the actin cytoskeleton and the MAPK signalling pathway. A proteomic analysis of the serum exosomes from patients with moyamoya disease showed that 859 shared proteins were detected in the serum exosomes from patients with ischaemic and haemorrhagic moyamoya disease, and 231 of which were different from those in the healthy controls. A bioinformatics analysis of these proteins revealed a protein imbalance and actin dynamics disorder related to cell growth and maintenance in moyamoya disease ¹⁶. Ren et al.¹⁷ found both MMD patient and Neo1 (neogenin) mutant mice exhibited altered gene expression in their cortex in proteins critical for axon guidance ,suggesting that Neo1 relevant axon guidance pathway may play important roles in regulating MMD-like vasculopathy. Previous studies have shown that the MAPK signaling pathway plays an important role in the pathogenesis of moyamoya disease¹⁸. These finding further supported our results that plasma exosomal miRNAs, as potential biomarkers, which contributes to the pathogenesis of moyamoya disease.

Moreover, we found miR-1306-5p, miR-196b-5p, miR-19a-3p, miR-22-3p, miR-320b, miR-34a-5p, miR-485-3p, miR-489-3p, miR-487b-3p, and miR-501-3p had high sensitivity and specificity for predicting moyamoya disease, and may serve as biomarkers of moyamoya disease. Transfection of miR-1306-5p mimics eliminated the inhibitory effect of SNHG7-003 overexpression on the proliferation and migration of vascular smooth muscle cells ¹⁹. MiR-34a directly regulates the expression of the target gene sirtuin 1 (SIRT1), which interacts with C2DAT1 to affect the proliferation and migration of vascular smooth muscle cells ²⁰. MiR-34a-5p participates in autophagy of human coronary artery endothelial cells induced by chronic intermittent hypoxia through the Bcl-2/Beclin1 signalling transduction pathway ²¹. MiRNA-34a-5p reduces the expression of VEGFA in endometrial stem cells involved in the pathogenesis of endometriosis ²². In addition, microRNA-34a promotes endothelial dysfunction and mitochondrial-mediated apoptosis in a mouse model of acute lung injury ²³. Under hypoxia, an inhibitor of miR-485-3p can promote the proliferation of human microvascular endothelial cells ²⁴. High expression of exosomal miR-501–3p promotes vascular sclerosis ²⁵. miR-22-3p can regulate human artery vascular smooth muscle cell proliferation and migration by targeting HMGB1, and may be therapeutic targets in the treatment of human arteriosclerosis obliterans²⁶. Dysregulation Serum miR-19a-3p is a Diagnostic Biomarker for Asymptomatic Carotid Artery Stenosis and a Promising Predictor of Cerebral Ischemia Events²⁷. miR-320b is a specific serum marker for carotid atherosclerosis and vulnerable plaque, which can be used to assist in the diagnosis of cerebrovascular diseases²⁸. These previous results support the findings of our study, in which the imbalance of miRNA expression has an impact on moyamoya disease, but it still needs to be confirmed by protein level and mechanism-based analysis. miR-196b-5p, miR-489-3p, miR-489-3p are first reported to be related to moyamoya disease, and more studies are needed to explore the mechanism of action of these miRNAs on moyamoya diseas.

In this study, we identified for the first time the miRNA expression profile of plasma exosomes in patients with moyamoya disease by high-throughput sequencing and found that the regulation of the actin cytoskeleton pathway may be involved in the pathogenesis of moyamoya disease. Moreover, this study found that some exosomal miRNAs, such as miR-1306-5p, miR-196b-5p, miR-19a-3p, miR-22-3p, miR-320b, miR-34a-5p, miR-485-3p, miR-489-3p, miR-487b-3p, and miR-501-3p might serve as are potential biomarkers for the diagnosis of moyamoya disease in future studies and mediate the pathogenesis.

Acknowledgments

Thanks for every patient participated in this study. It is their disease data that lays the foundation for the subsequent treatment of patients.

Data availability statements

The data that support the findings of this study are available from the corresponding author upon reasonable request. The raw data files of high-throughput sequencing were uploaded to https://zenodo.org/.DOI10.5281/zenodo.5800422.

Consent for publication

All participants in the study agreed to be published.

Author Contributions

Da Huang: Conceptualization, Methodology, Writing - Original Draft. Hui Qi: Methodology, Formal analysis, Writing - Original Draft. Hongchun Yang Investigation, Visualization. Meng chen: Software, Formal analysis.

Funding

This work was supported by Shenzhen Municipal Health and Family Planning System Scientific Research Project No. SZXJ2017052.

Conflict of Interest

The author declares that there is no conflict of interest.

Table 1 Clinical information of the samples

	NC	MMD	P-value
Male sex	5(50)	5(56%)	0.967
Age	46.1±6.4	48.9±12.1	0.603
WBC(×10⁹)	6.08±1.24	7.23±1.37	0.169
RBC(×10⁹)	4.45±0.32	4.45±0.41	0.967
Hb(mmol/L)	133±7.1	132±13	0.791
Glu(mmol/L)	5.31±0.62	5.21±0.65	0.729
K⁺(mmol/L)	4.02±0.21	3.95±0.21	0.456
Na⁺(mmol/L)	139.7±3.4	141.1±2.5	0.306
ALT(U/L)	19.5±6.6	24.45±15.18	0.364
TB(umol/L)	9.38±3.03	8.58±3.01	0.561
DB(umol/L)	1.92±0.19	1.75±0.39	0.232
TC(mg/DL)	3.63±1.03	3.88±1.57	0.678
TG(mg/DL)	1.41±0.69	1.54±0.87	0.737
HDL(mg/DL)	0.99±0.07	0.97±0.27	0.85
LDL(mg/DL)	2.88±0.63	3.02±0.77	0.662

Table 2 Sequencing data output statistics and quality control

Sample Name		Raw Reads	Clean Reads	Q20	Q30	GC%
	MMD1	13394601	11470036	0.994038	0.979452	0.541002
	MMD2	16789838	15597279	0.993253	0.977721	0.543984
	MMD3	19767133	14471822	0.993802	0.979251	0.541744
	MMD4	10192610	8096998	0.994197	0.979526	0.552548
	MMD5	13430381	11318666	0.993618	0.978735	0.542509
	MMD6	19573465	17755102	0.994607	0.98019	0.538964
	MMD7	20702651	17529882	0.99429	0.980239	0.527753
	MMD8	18527733	17109638	0.99283	0.977045	0.502724
	MMD9	21319758	20473411	0.993715	0.978213	0.487527
	NC1	12058148	11396788	0.984477	0.949508	0.552691
	NC2	37649858	36311886	0.990424	0.966974	0.548631
	NC3	21111513	18796751	0.989343	0.965364	0.566005
	NC4	35556828	29895610	0.986185	0.954861	0.548253
	NC5	11792452	11097666	0.985199	0.951597	0.559581
	NC6	15052720	4073973	0.992414	0.974378	0.585476
	NC7	11093372	5542318	0.992088	0.973208	0.580912
	NC8	10618213	7722455	0.991523	0.972106	0.588273
	NC9	11282238	7778548	0.992302	0.974028	0.582235
	NC10	30780243	20582756	0.992305	0.974105	0.583265

No competing interests reported.

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High-throughput sequencing reveals that microRNA from exosomes as potential pathogenetic factors and biomarkers of moyamoya disease

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Version 2

Abstract

Figures

Key Message

1 Introduction

2 Methods

2.1 Patients

2.2 Isolation and identification of exosomes

2.3 RNA library construction and sequencing

2.4 Data Processing and Bioinformatics Analysis

2.5 Real‑time quantitative PCR (RT-QPCR)

2.6 Statistical analysis

3 Results

3.1 Characterization of exosomes obtained from moyamoya disease patient plasma

3.2 GO and KEGG analyses of the target genes of differentially expressed miRNAs

3.3 Prognosis of potential biomarkers by the receiver operating characteristic curve and area under curve

4 Discussion

5 Conclusions

Declarations

Acknowledgments

Data availability statements

Consent for publication

Author Contributions

Funding

Conflict of Interest

Tables

Additional Declarations

Supplementary Files

Status:

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